二维指数熵图像阈值选取方法及其快速算法

二维Shannon信息熵法是图像阈值分割中常用的经典算法，但存在着不足。为此，提出了一种二维指数信息熵阈值选取方法，克服了对数熵的不足,将对数改为指数，提高了速度。同时给出了一种二维指数熵阈值选取的快速算法，通过改变二维直方图的区域划分，将二维阈值转换为一维，运行时间不到原算法的1%。实验结果表明，该算法能快速准确地实现图像分割。

Method of thresholding using two-dimensional exponent entropy and its fast algorithm

The two-dimensional Shannon's information entropy is a classical and commonly used image segmentation method,but there still are some disadvantages involved.Then,a two-dimensional exponent information entropy method for threshold selection was proposed here,which could overcome the disadvantages in the Shannon's entropy.The logarithm entropy was changed into the exponent entropy to get a higher speed.Meanwhile,a fast algorithm of two-dimensional exponent entropy thresholding method was also given,which changed the two-dimensional threshold into one-dimensional by using new area partition method,and the speed of the algorithm was no less than 1% of the original algorithm.The results of the experiment indicate that the proposed algorithm has high speed of calculation and good segmentation quality.